Suppose that a Bayesian spam filter is trained on a set of 5000 spam messages and 7500 messages that are not spam. The word "achievement" appears in 1000 spam messages and 150 messages that are not spam, while the word "research" appears in 800 spam messages and 300 messages that are not spam. Estimate the probability that a received message containing both the words "achievement" and "research" is spam. Will the message be rejected as spam if the threshold for rejecting spam is 0.9?

Suppose that a Bayesian spam filter is trained on a set of 5000 spam messages and 7500 messages that are not spam. The word "achievement" appears in 1000 spam messages and 150 messages that are not spam, while the word "research" appears in 800 spam messages and 300 messages that are not spam. Estimate the probability that a received message containing both the words "achievement" and "research" is spam. Will the message be rejected as spam if the threshold for rejecting spam is 0.9?

Name: Suppose that a Bayesian spam filter is trained on a set of 5000 spam
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Description: Suppose that a Bayesian spam filter is trained on a set of 5000 spam messages and 7500messages that are not spam. The word "achievement" appears in 1000 spam messages and 150messages that are not spam, while the word “research" appears in 800 spam m

A First Course in Probability (10th Edition)

10th Edition

ISBN:9780134753119

Author:Sheldon Ross

Publisher:Sheldon Ross

Chapter1: Combinatorial Analysis

Section: Chapter Questions

Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...

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